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Impact crater recognition methods: A review.

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Title: Impact crater recognition methods: A review.
Authors: Chen, Dong1 (AUTHOR), Hu, Fan1 (AUTHOR), Zhang, Liqiang2 (AUTHOR), Wu, Yunzhao3 (AUTHOR), Du, Jianli3,4 (AUTHOR) dujianli@pmo.ac.cn, Peethambaran, Jiju5 (AUTHOR)
Source: SCIENCE CHINA Earth Sciences. Jun2024, Vol. 67 Issue 6, p1719-1742. 24p.
Subjects: Impact craters, Inner planets, Deep learning, Remote sensing, Machine learning
Abstract: Impact craters are formed due to the high-speed collisions between small to medium-sized celestial bodies. Impact is the most significant driving force in the evolution of celestial bodies, and the impact craters provide crucial insights into the formation, evolution, and impact history of celestial bodies. In this paper, we present a detailed review of the characteristics of impact craters, impact crater remote sensing data, recognition algorithms, and applications related to impact craters. We first provide a detailed description of the geometric texture, illumination, and morphology characteristics observed in remote sensing data of craters. Then we summarize the remote sensing data and cataloging databases for the four terrestrial planets (i.e., the Moon, Mars, Mercury, and Venus), as well as the impact craters on Ceres. Subsequently, we study the advancement achieved in the traditional methods, machine learning methods, and deep learning methods applied to the classification, segmentation, and recognition of impact craters. Furthermore, based on the analysis results, we discuss the existing challenges in impact crater recognition and suggest some solutions. Finally, we explore the implementation of impact crater detection algorithms and provide a forward-looking perspective. [ABSTRACT FROM AUTHOR]
Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Impact+craters%22">Impact craters</searchLink><br /><searchLink fieldCode="DE" term="%22Inner+planets%22">Inner planets</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Impact craters are formed due to the high-speed collisions between small to medium-sized celestial bodies. Impact is the most significant driving force in the evolution of celestial bodies, and the impact craters provide crucial insights into the formation, evolution, and impact history of celestial bodies. In this paper, we present a detailed review of the characteristics of impact craters, impact crater remote sensing data, recognition algorithms, and applications related to impact craters. We first provide a detailed description of the geometric texture, illumination, and morphology characteristics observed in remote sensing data of craters. Then we summarize the remote sensing data and cataloging databases for the four terrestrial planets (i.e., the Moon, Mars, Mercury, and Venus), as well as the impact craters on Ceres. Subsequently, we study the advancement achieved in the traditional methods, machine learning methods, and deep learning methods applied to the classification, segmentation, and recognition of impact craters. Furthermore, based on the analysis results, we discuss the existing challenges in impact crater recognition and suggest some solutions. Finally, we explore the implementation of impact crater detection algorithms and provide a forward-looking perspective. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11430-023-1284-9
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        Text: English
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        Type: general
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      – SubjectFull: Deep learning
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              Text: Jun2024
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